{"id":"W4410635325","doi":"10.1016/j.envsoft.2025.106534","title":"Ice-jam flood predictions using an interpretable machine learning approach","year":2025,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada; Global Institute for Water Security; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Global Institute for Water Security, University of Saskatchewan; University of Saskatchewan","keywords":"Flood myth; Machine learning; Computer science; Artificial intelligence; Geography; Archaeology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005394365,0.0005926826,0.0003008667,0.0005341794,0.0002423192,0.001046434,0.0005793601,0.0008067018,0.002115434],"category_scores_gemma":[0.003475311,0.0003123944,0.0005072189,0.0002807723,0.0002758164,0.0009971785,0.0004334528,0.001032495,0.0003977775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005488385,"about_ca_system_score_gemma":0.0004670561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006343476,"about_ca_topic_score_gemma":0.006786277,"domain_scores_codex":[0.9998479,0.00004786162,0.00001384817,0.00003963685,0.00003612068,0.00001469342],"domain_scores_gemma":[0.999069,0.000603041,0.00007901566,0.00006875084,0.0001578651,0.00002236285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008465259,0.0001027078,0.002278548,0.00002537576,0.00003153934,0.00008383465,0.00004487123,0.9493291,0.002356551,0.001567176,0.0008389787,0.04325673],"study_design_scores_gemma":[0.000001959065,0.000005063795,0.0001967397,0.000002002622,0.000002404705,0.00000207998,0.00000296411,0.998771,0.000295746,0.0006607679,0.00005735879,0.000001752986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3316899,0.0002144375,0.6552637,0.0009807451,0.0001400196,0.00008337422,0.0009786551,0.004539246,0.006109972],"genre_scores_gemma":[0.9447342,0.00007096694,0.05320783,0.00006351118,0.000039279,0.00004774274,0.0004979165,0.00008352241,0.001255058],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006343476,"threshold_uncertainty_score":0.01261312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01389199834786998,"score_gpt":0.2248949261730859,"score_spread":0.211002927825216,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}